Datasets:

tyluan
/

Endovis2017

license: cc-by-4.0
task_categories:
  - image-segmentation
tags:
  - medical
  - surgical-instruments
  - endoscopy
  - robotic-surgery
  - image-segmentation
pretty_name: Endovis 2017 - Robotic Instrument Segmentation
size_categories:
  - 1K<n<10K
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype: image
    - name: image_id
      dtype: string
    - name: split
      dtype: string
    - name: file_name
      dtype: string
    - name: relative_path
      dtype: string
    - name: sequence_id
      dtype: int64
  splits:
    - name: train
      num_bytes: 1889651647
      num_examples: 1800
    - name: val
      num_bytes: 945876232
      num_examples: 901
  download_size: 2026707643
  dataset_size: 2835527879
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: val
        path: data/val-*

Dataset Card for Endovis2017

Dataset Description

Dataset Summary

The Endovis2017 dataset contains preprocessed data for surgical instrument segmentation in robotic endoscopic procedures. This dataset was part of the MICCAI 2017 EndoVis Challenge for robotic instrument segmentation.

The dataset includes high-resolution images from the da Vinci surgical system along with pixel-level segmentation annotations for surgical instruments. It is designed for training and evaluating computer vision models for surgical scene understanding and instrument tracking.

Supported Tasks

Image Segmentation: Pixel-level segmentation of surgical instruments in endoscopic images
Medical Image Analysis: Understanding surgical scenes and instrument types
Computer-Assisted Surgery: Real-time instrument detection and tracking

Languages

Not applicable (image dataset)

Dataset Structure

Data Instances

Each instance in the dataset contains:

{
    'image': PIL.Image,          # RGB endoscopic image
    'label': PIL.Image,          # Segmentation mask (grayscale)
    'image_id': str,             # Unique identifier
    'file_name': str,            # Original filename
    'split': str,                # 'train' or 'val'
    'relative_path': str,        # Path relative to dataset root
    'sequence_id': int           # Sequence/video ID (0 for train, 1-4 for val)
}

Data Fields

image: RGB image of size 640×480 or similar (varies by sequence)
label: Grayscale segmentation mask matching image dimensions
image_id: Unique string identifier for the image
file_name: Original filename (e.g., "frame000.png")
split: Dataset split ("train" or "val")
relative_path: Path relative to dataset root directory
sequence_id: Integer identifying the surgical sequence (0 for training, 1-4 for validation sequences)

Data Splits

Split	Examples
train	1,800
val	901
Total	2,701

The training set contains images from multiple surgical procedures, while the validation set is organized into 4 different sequences (val1-val4) representing different surgical scenarios.

Dataset Creation

Source Data

The dataset originates from the 2017 Robotic Instrument Segmentation Challenge held at MICCAI 2017.

Original Source: Zenodo Repository

Data Collection

Images were captured using the da Vinci surgical system during robotic-assisted surgical procedures. The dataset includes various instrument types and surgical scenarios to ensure model generalization.

Annotations

Pixel-level segmentation masks were manually annotated by experts. The annotations include:

Binary segmentation (instrument vs. background)
Part-level segmentation (shaft, wrist, claspers)
Instrument type classification

Personal and Sensitive Information

The dataset contains surgical video frames but does not include patient-identifiable information. All images show only the surgical field and instruments, not patients.

Considerations for Using the Data

Social Impact

This dataset enables research in computer-assisted surgery and robotic surgery, which can potentially:

Improve surgical outcomes through better instrument tracking
Enable automated surgical skill assessment
Advance autonomous surgical robotics

Bias and Limitations

Limited to da Vinci surgical system (may not generalize to other platforms)
Contains only certain types of surgical procedures
Annotation quality may vary across different sequences
Dataset size is relatively small compared to natural image datasets

Recommendations

Users should:

Test models on multiple surgical systems if deploying in production
Consider domain adaptation techniques for different surgical contexts
Validate performance on institution-specific data before clinical use
Be aware of potential biases toward specific instrument types and surgical scenarios

Usage

Loading the Dataset

from datasets import load_dataset

# Download and cache the full dataset
dataset = load_dataset("tyluan/Endovis2017")

# Access splits
train_data = dataset['train']
val_data = dataset['val']

# Get a sample
sample = train_data[0]
image = sample['image']  # PIL Image
label = sample['label']  # PIL Image (segmentation mask)

print(f"Image size: {image.size}")
print(f"Label size: {label.size}")

Streaming Mode (No Download)

For quick exploration without downloading the entire dataset:

from datasets import load_dataset

# Stream the dataset
dataset = load_dataset("tyluan/Endovis2017", streaming=True)

# Iterate over samples
for sample in dataset['train']:
    image = sample['image']
    label = sample['label']
    # Process sample...
    break  # Just show first sample

Using with PyTorch

from datasets import load_dataset
from torch.utils.data import DataLoader
from torchvision import transforms

# Load dataset
dataset = load_dataset("tyluan/Endovis2017", split="train")

# Define transforms
transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor(),
])

# Apply transforms
def apply_transforms(example):
    example['image'] = transform(example['image'])
    example['label'] = transform(example['label'])
    return example

dataset = dataset.map(apply_transforms)
dataset.set_format(type='torch', columns=['image', 'label'])

# Create DataLoader
dataloader = DataLoader(dataset, batch_size=8, shuffle=True)

# Iterate
for batch in dataloader:
    images = batch['image']  # Shape: [8, 3, 256, 256]
    labels = batch['label']  # Shape: [8, 1, 256, 256]
    # Train your model...
    break

Integration with EasyMedSeg

This dataset is part of the EasyMedSeg framework:

from dataloader.image import Endovis2017Dataset

# Download mode (recommended)
dataset = Endovis2017Dataset(
    mode='download',
    split='train',
    hf_repo_id='tyluan/Endovis2017'
)

# Streaming mode
from dataloader.image import Endovis2017StreamingDataset

streaming_dataset = Endovis2017StreamingDataset(
    split='val',
    shuffle=True
)

Additional Information

Dataset Curators

Original dataset curated by the MICCAI 2017 EndoVis Challenge organizers.

HuggingFace version prepared by the EasyMedSeg team.

Licensing Information

This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

When using this dataset, you must:

Give appropriate credit to the original authors
Provide a link to the license: https://creativecommons.org/licenses/by/4.0/
Indicate if changes were made

Citation Information

If you use this dataset in your research, please cite:

@article{allan2019endovis,
  title={2017 Robotic Instrument Segmentation Challenge},
  author={Allan, Max and Shvets, Alex and Kurmann, Thomas and Zhang, Zichen and Duggal, Rahul and Su, Yun-Hsuan and Rieke, Nicola and Laina, Iro and Kalavakonda, Niveditha and Bodenstedt, Sebastian and others},
  journal={arXiv preprint arXiv:1902.06426},
  year={2019}
}

Contributions

Thanks to:

MICCAI 2017 EndoVis Challenge organizers for creating the dataset
Original annotators for high-quality segmentation masks
EasyMedSeg team for preparing the HuggingFace version

Contact

For questions or issues with this HuggingFace version, please open an issue in the EasyMedSeg repository.

For questions about the original dataset, refer to the challenge website or the Zenodo repository.